Overview

Dataset statistics

Number of variables24
Number of observations26555
Missing cells235
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 MiB
Average record size in memory294.9 B

Variable types

NUM21
CAT3

Warnings

id has a high cardinality: 113 distinct values High cardinality
yrs is highly correlated with mos and 1 other fieldsHigh correlation
mos is highly correlated with yrs and 1 other fieldsHigh correlation
age is highly correlated with mos and 1 other fieldsHigh correlation
id is uniformly distributed Uniform
Avg_deg has 4700 (17.7%) zeros Zeros

Reproduction

Analysis started2020-10-22 09:47:47.969366
Analysis finished2020-10-22 09:48:46.282988
Duration58.31 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct113
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size207.6 KiB
132_9a
 
235
431_15a
 
235
516_17a
 
235
123_9a
 
235
317_13a
 
235
Other values (108)
25380 
ValueCountFrequency (%) 
132_9a2350.9%
 
431_15a2350.9%
 
516_17a2350.9%
 
123_9a2350.9%
 
317_13a2350.9%
 
125_9a2350.9%
 
324_13a2350.9%
 
225_11a2350.9%
 
209_11a2350.9%
 
110_9a2350.9%
 
408_15a2350.9%
 
131_9a2350.9%
 
216_11a2350.9%
 
335_13a2350.9%
 
522_17a2350.9%
 
224_11a2350.9%
 
518_17a2350.9%
 
121_9a2350.9%
 
129_9a2350.9%
 
428_15a2350.9%
 
228_11a2350.9%
 
512_17a2350.9%
 
207_11a2350.9%
 
525_17a2350.9%
 
319_13a2350.9%
 
Other values (88)2068077.9%
 
2020-10-22T02:48:46.394332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-22T02:48:46.522059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.831858407
Min length6

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
14441524.5%
 
_2655514.6%
 
a2655514.6%
 
21974010.9%
 
3176259.7%
 
5110456.1%
 
084604.7%
 
777554.3%
 
972854.0%
 
463453.5%
 
630551.7%
 
825851.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number12831070.7%
 
Connector Punctuation2655514.6%
 
Lowercase Letter2655514.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14441534.6%
 
21974015.4%
 
31762513.7%
 
5110458.6%
 
084606.6%
 
777556.0%
 
972855.7%
 
463454.9%
 
630552.4%
 
825852.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_26555100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a26555100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common15486585.4%
 
Latin2655514.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
14441528.7%
 
_2655517.1%
 
21974012.7%
 
31762511.4%
 
5110457.1%
 
084605.5%
 
777555.0%
 
972854.7%
 
463454.1%
 
630552.0%
 
825851.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a26555100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII181420100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
14441524.5%
 
_2655514.6%
 
a2655514.6%
 
21974010.9%
 
3176259.7%
 
5110456.1%
 
084604.7%
 
777554.3%
 
972854.0%
 
463453.5%
 
630551.7%
 
825851.4%
 

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size207.6 KiB
female
14570 
male
11985 
ValueCountFrequency (%) 
female1457054.9%
 
male1198545.1%
 
2020-10-22T02:48:46.617019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-22T02:48:46.671337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:46.752066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.097345133
Min length4

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e4112530.4%
 
m2655519.6%
 
a2655519.6%
 
l2655519.6%
 
f1457010.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter135360100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e4112530.4%
 
m2655519.6%
 
a2655519.6%
 
l2655519.6%
 
f1457010.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin135360100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e4112530.4%
 
m2655519.6%
 
a2655519.6%
 
l2655519.6%
 
f1457010.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135360100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e4112530.4%
 
m2655519.6%
 
a2655519.6%
 
l2655519.6%
 
f1457010.8%
 

mos
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.9853982
Minimum109.5
Maximum207.7
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:46.864669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum109.5
5-th percentile110.1
Q1134.18
median158.3
Q3183
95-th percentile207
Maximum207.7
Range98.2
Interquartile range (IQR)48.82

Descriptive statistics

Standard deviation32.4622853
Coefficient of variation (CV)0.2067853805
Kurtosis-1.142946633
Mean156.9853982
Median Absolute Deviation (MAD)24.5
Skewness0.1518553015
Sum4168747.25
Variance1053.799967
MonotocityNot monotonic
2020-10-22T02:48:46.985551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
158.914105.3%
 
134.711754.4%
 
158.59403.5%
 
158.39403.5%
 
134.87052.7%
 
110.57052.7%
 
183.17052.7%
 
1837052.7%
 
2064701.8%
 
134.024701.8%
 
206.64701.8%
 
134.614701.8%
 
109.54701.8%
 
110.34701.8%
 
110.24701.8%
 
158.84701.8%
 
206.74701.8%
 
134.54701.8%
 
206.84701.8%
 
157.44701.8%
 
182.64701.8%
 
158.44701.8%
 
109.74701.8%
 
183.34701.8%
 
110.44701.8%
 
Other values (45)1128042.5%
 
ValueCountFrequency (%) 
109.54701.8%
 
109.74701.8%
 
109.92350.9%
 
110.12350.9%
 
110.24701.8%
 
110.34701.8%
 
110.44701.8%
 
110.57052.7%
 
110.64701.8%
 
110.72350.9%
 
ValueCountFrequency (%) 
207.74701.8%
 
207.42350.9%
 
207.12350.9%
 
207.072350.9%
 
2072350.9%
 
206.84701.8%
 
206.712350.9%
 
206.74701.8%
 
206.64701.8%
 
206.52350.9%
 

yrs
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)0.2%
Missing235
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean13.019375
Minimum9.07
Maximum17.2
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:47.100457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9.07
5-th percentile9.12
Q111.125
median13.115
Q315.165
95-th percentile17.14
Maximum17.2
Range8.13
Interquartile range (IQR)4.04

Descriptive statistics

Standard deviation2.694338554
Coefficient of variation (CV)0.2069483792
Kurtosis-1.148635777
Mean13.019375
Median Absolute Deviation (MAD)2.02
Skewness0.1362009361
Sum342669.95
Variance7.259460246
MonotocityNot monotonic
2020-10-22T02:48:47.224945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11.1618807.1%
 
13.1616456.2%
 
13.1111754.4%
 
13.139403.5%
 
11.157052.7%
 
15.27052.7%
 
11.17052.7%
 
17.127052.7%
 
17.117052.7%
 
15.167052.7%
 
9.147052.7%
 
15.187052.7%
 
9.167052.7%
 
11.137052.7%
 
15.17052.7%
 
9.137052.7%
 
13.034701.8%
 
17.074701.8%
 
11.014701.8%
 
13.124701.8%
 
13.094701.8%
 
17.154701.8%
 
9.074701.8%
 
17.24701.8%
 
17.134701.8%
 
Other values (31)799030.1%
 
ValueCountFrequency (%) 
9.074701.8%
 
9.082350.9%
 
9.092350.9%
 
9.12350.9%
 
9.122350.9%
 
9.137052.7%
 
9.147052.7%
 
9.154701.8%
 
9.167052.7%
 
9.172350.9%
 
ValueCountFrequency (%) 
17.24701.8%
 
17.182350.9%
 
17.154701.8%
 
17.142350.9%
 
17.134701.8%
 
17.127052.7%
 
17.117052.7%
 
17.082350.9%
 
17.074701.8%
 
17.062350.9%
 

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.87610619
Minimum9
Maximum17
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:47.320981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9
Q111
median13
Q315
95-th percentile17
Maximum17
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.691014714
Coefficient of variation (CV)0.2089928954
Kurtosis-1.134213442
Mean12.87610619
Median Absolute Deviation (MAD)2
Skewness0.156394398
Sum341925
Variance7.241560194
MonotocityIncreasing
2020-10-22T02:48:47.411455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
13658024.8%
 
11658024.8%
 
17493518.6%
 
9446516.8%
 
15399515.0%
 
ValueCountFrequency (%) 
9446516.8%
 
11658024.8%
 
13658024.8%
 
15399515.0%
 
17493518.6%
 
ValueCountFrequency (%) 
17493518.6%
 
15399515.0%
 
13658024.8%
 
11658024.8%
 
9446516.8%
 

label_enc
Real number (ℝ≥0)

Distinct235
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117
Minimum0
Maximum234
Zeros113
Zeros (%)0.4%
Memory size207.6 KiB
2020-10-22T02:48:47.517039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q158
median117
Q3176
95-th percentile223
Maximum234
Range234
Interquartile range (IQR)118

Descriptive statistics

Standard deviation67.83931977
Coefficient of variation (CV)0.5798232459
Kurtosis-1.200043464
Mean117
Median Absolute Deviation (MAD)59
Skewness0
Sum3106935
Variance4602.173307
MonotocityNot monotonic
2020-10-22T02:48:47.636445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2231130.4%
 
2121130.4%
 
1651130.4%
 
1491130.4%
 
1331130.4%
 
1171130.4%
 
1011130.4%
 
851130.4%
 
691130.4%
 
531130.4%
 
371130.4%
 
211130.4%
 
51130.4%
 
2281130.4%
 
1961130.4%
 
2111130.4%
 
1801130.4%
 
1641130.4%
 
1481130.4%
 
1321130.4%
 
1161130.4%
 
1001130.4%
 
841130.4%
 
681130.4%
 
521130.4%
 
Other values (210)2373089.4%
 
ValueCountFrequency (%) 
01130.4%
 
11130.4%
 
21130.4%
 
31130.4%
 
41130.4%
 
51130.4%
 
61130.4%
 
71130.4%
 
81130.4%
 
91130.4%
 
ValueCountFrequency (%) 
2341130.4%
 
2331130.4%
 
2321130.4%
 
2311130.4%
 
2301130.4%
 
2291130.4%
 
2281130.4%
 
2271130.4%
 
2261130.4%
 
2251130.4%
 

Avg_deg
Real number (ℝ≥0)

ZEROS

Distinct21856
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.1013034
Minimum0
Maximum129.577232
Zeros4700
Zeros (%)17.7%
Memory size207.6 KiB
2020-10-22T02:48:47.757085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q194.50483566
median96.64390791
Q398.34064402
95-th percentile101.1586301
Maximum129.577232
Range129.577232
Interquartile range (IQR)3.835808359

Descriptive statistics

Standard deviation37.22828293
Coefficient of variation (CV)0.4647650082
Kurtosis0.8380526271
Mean80.1013034
Median Absolute Deviation (MAD)1.879825482
Skewness-1.675375517
Sum2127090.112
Variance1385.94505
MonotocityNot monotonic
2020-10-22T02:48:47.882739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0470017.7%
 
98.028072671< 0.1%
 
101.30283711< 0.1%
 
98.402723531< 0.1%
 
96.641346851< 0.1%
 
96.005129791< 0.1%
 
95.224644421< 0.1%
 
96.98052131< 0.1%
 
95.55664071< 0.1%
 
96.84074161< 0.1%
 
100.7401171< 0.1%
 
98.773533781< 0.1%
 
97.377831< 0.1%
 
94.655006391< 0.1%
 
97.150269351< 0.1%
 
101.10454741< 0.1%
 
96.183001961< 0.1%
 
95.345389991< 0.1%
 
95.432202341< 0.1%
 
93.794007931< 0.1%
 
98.456468721< 0.1%
 
97.187927481< 0.1%
 
97.611325261< 0.1%
 
94.452363161< 0.1%
 
97.763784511< 0.1%
 
Other values (21831)2183182.2%
 
ValueCountFrequency (%) 
0470017.7%
 
80.068781241< 0.1%
 
80.285664171< 0.1%
 
80.329836641< 0.1%
 
80.371869711< 0.1%
 
81.097760731< 0.1%
 
81.269870341< 0.1%
 
81.317861061< 0.1%
 
81.491307091< 0.1%
 
81.674093121< 0.1%
 
ValueCountFrequency (%) 
129.5772321< 0.1%
 
125.5896071< 0.1%
 
124.54538381< 0.1%
 
124.47855221< 0.1%
 
123.83256081< 0.1%
 
123.36753031< 0.1%
 
122.99078881< 0.1%
 
121.69062741< 0.1%
 
121.64987031< 0.1%
 
121.6471161< 0.1%
 

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
female
14335 
male
12220 
ValueCountFrequency (%) 
female1433554.0%
 
male1222046.0%
 
2020-10-22T02:48:47.995340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-22T02:48:48.064495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:48.174021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.079646018
Min length4

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e4089030.3%
 
m2655519.7%
 
a2655519.7%
 
l2655519.7%
 
f1433510.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter134890100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e4089030.3%
 
m2655519.7%
 
a2655519.7%
 
l2655519.7%
 
f1433510.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin134890100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e4089030.3%
 
m2655519.7%
 
a2655519.7%
 
l2655519.7%
 
f1433510.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII134890100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e4089030.3%
 
m2655519.7%
 
a2655519.7%
 
l2655519.7%
 
f1433510.6%
 

peermindset
Real number (ℝ≥0)

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.230088496
Minimum1
Maximum4.583333333
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:48.278703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.166666667
Q11.75
median2.166666667
Q32.583333333
95-th percentile3.416666667
Maximum4.583333333
Range3.583333333
Interquartile range (IQR)0.8333333333

Descriptive statistics

Standard deviation0.6907896436
Coefficient of variation (CV)0.3097588481
Kurtosis0.6378849792
Mean2.230088496
Median Absolute Deviation (MAD)0.4166666667
Skewness0.5726343017
Sum59220
Variance0.4771903318
MonotocityNot monotonic
2020-10-22T02:48:48.391580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%) 
2.16666666723508.8%
 
2.2518807.1%
 
2.41666666718807.1%
 
214105.3%
 
2.33333333311754.4%
 
2.66666666711754.4%
 
1.91666666711754.4%
 
1.58333333311754.4%
 
2.759403.5%
 
19403.5%
 
1.259403.5%
 
2.59403.5%
 
2.0833333339403.5%
 
1.4166666679403.5%
 
2.8333333337052.7%
 
1.8333333337052.7%
 
1.3333333337052.7%
 
3.257052.7%
 
1.6666666677052.7%
 
2.5833333337052.7%
 
3.4166666677052.7%
 
1.754701.8%
 
34701.8%
 
1.54701.8%
 
1.1666666672350.9%
 
Other values (9)21158.0%
 
ValueCountFrequency (%) 
19403.5%
 
1.0833333332350.9%
 
1.1666666672350.9%
 
1.259403.5%
 
1.3333333337052.7%
 
1.4166666679403.5%
 
1.54701.8%
 
1.58333333311754.4%
 
1.6666666677052.7%
 
1.754701.8%
 
ValueCountFrequency (%) 
4.5833333332350.9%
 
4.1666666672350.9%
 
3.9166666672350.9%
 
3.6666666672350.9%
 
3.52350.9%
 
3.4166666677052.7%
 
3.257052.7%
 
3.1666666672350.9%
 
3.0833333332350.9%
 
34701.8%
 

persmindset
Real number (ℝ≥0)

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.874041298
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:48.500681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.333333333
median1.833333333
Q32.333333333
95-th percentile3.166666667
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6696627843
Coefficient of variation (CV)0.3573361937
Kurtosis0.3121634165
Mean1.874041298
Median Absolute Deviation (MAD)0.5
Skewness0.7534230509
Sum49765.16667
Variance0.4484482447
MonotocityNot monotonic
2020-10-22T02:48:48.625201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
1376014.2%
 
2305511.5%
 
2.333333333305511.5%
 
1.833333333282010.6%
 
1.66666666723508.8%
 
1.33333333323508.8%
 
1.523508.8%
 
2.66666666714105.3%
 
1.16666666714105.3%
 
2.59403.5%
 
2.8333333337052.7%
 
34701.8%
 
3.3333333334701.8%
 
3.52350.9%
 
1.62350.9%
 
3.8333333332350.9%
 
42350.9%
 
3.1666666672350.9%
 
2.1666666672350.9%
 
ValueCountFrequency (%) 
1376014.2%
 
1.16666666714105.3%
 
1.33333333323508.8%
 
1.523508.8%
 
1.62350.9%
 
1.66666666723508.8%
 
1.833333333282010.6%
 
2305511.5%
 
2.1666666672350.9%
 
2.333333333305511.5%
 
ValueCountFrequency (%) 
42350.9%
 
3.8333333332350.9%
 
3.52350.9%
 
3.3333333334701.8%
 
3.1666666672350.9%
 
34701.8%
 
2.8333333337052.7%
 
2.66666666714105.3%
 
2.59403.5%
 
2.333333333305511.5%
 

needforapproval
Real number (ℝ≥0)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.630162242
Minimum1.875
Maximum5.375
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:48.763158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.875
5-th percentile2.25
Q13.125
median3.625
Q34.125
95-th percentile4.875
Maximum5.375
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7486225382
Coefficient of variation (CV)0.2062228871
Kurtosis-0.2277842296
Mean3.630162242
Median Absolute Deviation (MAD)0.5
Skewness-0.01339772593
Sum96398.95833
Variance0.5604357047
MonotocityNot monotonic
2020-10-22T02:48:48.892529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
3.25282010.6%
 
4.12523508.8%
 
3.87518807.1%
 
3.7516456.2%
 
3.37516456.2%
 
3.62514105.3%
 
3.12514105.3%
 
4.2514105.3%
 
3.514105.3%
 
2.87514105.3%
 
4.37514105.3%
 
4.7511754.4%
 
37052.7%
 
2.257052.7%
 
5.257052.7%
 
2.6254701.8%
 
44701.8%
 
4.54701.8%
 
1.8754701.8%
 
2.754701.8%
 
2.3754701.8%
 
4.8754701.8%
 
5.3752350.9%
 
2.8333333332350.9%
 
4.6252350.9%
 
Other values (2)4701.8%
 
ValueCountFrequency (%) 
1.8754701.8%
 
22350.9%
 
2.257052.7%
 
2.3754701.8%
 
2.52350.9%
 
2.6254701.8%
 
2.754701.8%
 
2.8333333332350.9%
 
2.87514105.3%
 
37052.7%
 
ValueCountFrequency (%) 
5.3752350.9%
 
5.257052.7%
 
4.8754701.8%
 
4.7511754.4%
 
4.6252350.9%
 
4.54701.8%
 
4.37514105.3%
 
4.2514105.3%
 
4.12523508.8%
 
44701.8%
 

needforbelonging
Real number (ℝ≥0)

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.83539823
Minimum1.8
Maximum5.3
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:49.000650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.8
Q13.4
median3.9
Q34.3
95-th percentile4.8
Maximum5.3
Range3.5
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.6454756085
Coefficient of variation (CV)0.1682942865
Kurtosis-0.03541776357
Mean3.83539823
Median Absolute Deviation (MAD)0.4
Skewness-0.2727762032
Sum101849
Variance0.4166387612
MonotocityNot monotonic
2020-10-22T02:48:49.524430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
4.123508.8%
 
3.621158.0%
 
4.418807.1%
 
3.816456.2%
 
416456.2%
 
3.114105.3%
 
4.214105.3%
 
3.311754.4%
 
3.911754.4%
 
3.711754.4%
 
3.29403.5%
 
4.69403.5%
 
2.89403.5%
 
3.59403.5%
 
4.79403.5%
 
4.39403.5%
 
4.57052.7%
 
37052.7%
 
57052.7%
 
3.44701.8%
 
4.84701.8%
 
2.94701.8%
 
1.82350.9%
 
2.62350.9%
 
5.22350.9%
 
Other values (3)7052.7%
 
ValueCountFrequency (%) 
1.82350.9%
 
2.42350.9%
 
2.52350.9%
 
2.62350.9%
 
2.89403.5%
 
2.94701.8%
 
37052.7%
 
3.114105.3%
 
3.29403.5%
 
3.311754.4%
 
ValueCountFrequency (%) 
5.32350.9%
 
5.22350.9%
 
57052.7%
 
4.84701.8%
 
4.79403.5%
 
4.69403.5%
 
4.57052.7%
 
4.418807.1%
 
4.39403.5%
 
4.214105.3%
 

rejection
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.230088496
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:49.614984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.068546933
Coefficient of variation (CV)0.3308104202
Kurtosis-0.5260432424
Mean3.230088496
Median Absolute Deviation (MAD)0.5
Skewness-0.4075624365
Sum85775
Variance1.141792547
MonotocityNot monotonic
2020-10-22T02:48:49.704586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
4564021.2%
 
3.5517019.5%
 
3329012.4%
 
2.5329012.4%
 
225859.7%
 
4.521158.0%
 
518807.1%
 
118807.1%
 
1.57052.7%
 
ValueCountFrequency (%) 
118807.1%
 
1.57052.7%
 
225859.7%
 
2.5329012.4%
 
3329012.4%
 
3.5517019.5%
 
4564021.2%
 
4.521158.0%
 
518807.1%
 
ValueCountFrequency (%) 
518807.1%
 
4.521158.0%
 
4564021.2%
 
3.5517019.5%
 
3329012.4%
 
2.5329012.4%
 
225859.7%
 
1.57052.7%
 
118807.1%
 

coping_mad
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.014749263
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:49.791675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.666666667
Q12.333333333
median3
Q33.666666667
95-th percentile4.333333333
Maximum5
Range4
Interquartile range (IQR)1.333333333

Descriptive statistics

Standard deviation0.8694575624
Coefficient of variation (CV)0.2884012854
Kurtosis-0.7633235912
Mean3.014749263
Median Absolute Deviation (MAD)0.6666666667
Skewness-0.186054618
Sum80056.66667
Variance0.7559564529
MonotocityNot monotonic
2020-10-22T02:48:49.890889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
3.666666667399515.0%
 
2.666666667399515.0%
 
4352513.3%
 
3.333333333282010.6%
 
3282010.6%
 
2.33333333325859.7%
 
1.66666666718807.1%
 
218807.1%
 
4.33333333314105.3%
 
1.3333333339403.5%
 
4.6666666672350.9%
 
52350.9%
 
12350.9%
 
ValueCountFrequency (%) 
12350.9%
 
1.3333333339403.5%
 
1.66666666718807.1%
 
218807.1%
 
2.33333333325859.7%
 
2.666666667399515.0%
 
3282010.6%
 
3.333333333282010.6%
 
3.666666667399515.0%
 
4352513.3%
 
ValueCountFrequency (%) 
52350.9%
 
4.6666666672350.9%
 
4.33333333314105.3%
 
4352513.3%
 
3.666666667399515.0%
 
3.333333333282010.6%
 
3282010.6%
 
2.666666667399515.0%
 
2.33333333325859.7%
 
218807.1%
 

coping_sad
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.203539823
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:49.989106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.333333333
Q12.333333333
median3.333333333
Q34
95-th percentile4.666666667
Maximum5
Range4
Interquartile range (IQR)1.666666667

Descriptive statistics

Standard deviation1.014596849
Coefficient of variation (CV)0.3167111711
Kurtosis-0.8203764099
Mean3.203539823
Median Absolute Deviation (MAD)0.6666666667
Skewness-0.24557106
Sum85070
Variance1.029406766
MonotocityNot monotonic
2020-10-22T02:48:50.080810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
4446516.8%
 
3.666666667329012.4%
 
3.333333333282010.6%
 
2.666666667282010.6%
 
225859.7%
 
2.33333333321158.0%
 
4.66666666718807.1%
 
316456.2%
 
4.33333333314105.3%
 
1.66666666711754.4%
 
59403.5%
 
1.3333333337052.7%
 
17052.7%
 
ValueCountFrequency (%) 
17052.7%
 
1.3333333337052.7%
 
1.66666666711754.4%
 
225859.7%
 
2.33333333321158.0%
 
2.666666667282010.6%
 
316456.2%
 
3.333333333282010.6%
 
3.666666667329012.4%
 
4446516.8%
 
ValueCountFrequency (%) 
59403.5%
 
4.66666666718807.1%
 
4.33333333314105.3%
 
4446516.8%
 
3.666666667329012.4%
 
3.333333333282010.6%
 
316456.2%
 
2.666666667282010.6%
 
2.33333333321158.0%
 
225859.7%
 

coping_worried
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.510324484
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:50.173759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.666666667
median2.666666667
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)1.333333333

Descriptive statistics

Standard deviation0.8892297088
Coefficient of variation (CV)0.354228991
Kurtosis-0.4979569626
Mean2.510324484
Median Absolute Deviation (MAD)0.6666666667
Skewness0.1250096705
Sum66661.66667
Variance0.7907294751
MonotocityNot monotonic
2020-10-22T02:48:50.273378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
3399515.0%
 
2.666666667329012.4%
 
2.333333333329012.4%
 
3.333333333305511.5%
 
1.666666667305511.5%
 
2282010.6%
 
123508.8%
 
3.66666666716456.2%
 
1.33333333314105.3%
 
4.3333333337052.7%
 
47052.7%
 
52350.9%
 
ValueCountFrequency (%) 
123508.8%
 
1.33333333314105.3%
 
1.666666667305511.5%
 
2282010.6%
 
2.333333333329012.4%
 
2.666666667329012.4%
 
3399515.0%
 
3.333333333305511.5%
 
3.66666666716456.2%
 
47052.7%
 
ValueCountFrequency (%) 
52350.9%
 
4.3333333337052.7%
 
47052.7%
 
3.66666666716456.2%
 
3.333333333305511.5%
 
3399515.0%
 
2.666666667329012.4%
 
2.333333333329012.4%
 
2282010.6%
 
1.666666667305511.5%
 

rsqanxiety
Real number (ℝ≥0)

Distinct82
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.588495575
Minimum1.5
Maximum22.5
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:50.384508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.833333333
Q15.833333333
median8.666666667
Q311
95-th percentile15.75
Maximum22.5
Range21
Interquartile range (IQR)5.166666667

Descriptive statistics

Standard deviation3.874980313
Coefficient of variation (CV)0.451182664
Kurtosis0.4652664245
Mean8.588495575
Median Absolute Deviation (MAD)2.5
Skewness0.5578614383
Sum228067.5
Variance15.01547243
MonotocityNot monotonic
2020-10-22T02:48:50.505613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.83333333314105.3%
 
8.91666666711754.4%
 
7.1666666677052.7%
 
114701.8%
 
9.3333333334701.8%
 
11.166666674701.8%
 
8.0833333334701.8%
 
7.54701.8%
 
10.666666674701.8%
 
7.4166666674701.8%
 
4.6666666674701.8%
 
3.9166666674701.8%
 
12.254701.8%
 
3.6666666674701.8%
 
6.754701.8%
 
6.4166666674701.8%
 
4.3333333334701.8%
 
4.1666666674701.8%
 
64701.8%
 
17.083333334701.8%
 
2.8333333334701.8%
 
74701.8%
 
9.1666666674701.8%
 
11.252350.9%
 
1.9166666672350.9%
 
Other values (57)1339550.4%
 
ValueCountFrequency (%) 
1.52350.9%
 
1.9166666672350.9%
 
2.1666666672350.9%
 
2.252350.9%
 
2.52350.9%
 
2.8333333334701.8%
 
3.1666666672350.9%
 
3.3333333332350.9%
 
3.5833333332350.9%
 
3.6666666674701.8%
 
ValueCountFrequency (%) 
22.52350.9%
 
17.083333334701.8%
 
16.52350.9%
 
16.416666672350.9%
 
15.752350.9%
 
15.252350.9%
 
14.752350.9%
 
14.52350.9%
 
14.333333332350.9%
 
14.166666672350.9%
 

rsqanger
Real number (ℝ≥0)

Distinct71
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.892330383
Minimum1.333333333
Maximum15.33333333
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:50.618218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.333333333
5-th percentile2.416666667
Q13.666666667
median4.916666667
Q37.5
95-th percentile13.08333333
Maximum15.33333333
Range14
Interquartile range (IQR)3.833333333

Descriptive statistics

Standard deviation3.105658167
Coefficient of variation (CV)0.5270678941
Kurtosis0.8665536268
Mean5.892330383
Median Absolute Deviation (MAD)1.666666667
Skewness1.155233562
Sum156470.8333
Variance9.645112649
MonotocityNot monotonic
2020-10-22T02:48:50.735690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.8333333339403.5%
 
3.259403.5%
 
3.6666666679403.5%
 
4.259403.5%
 
3.57052.7%
 
2.4166666677052.7%
 
57052.7%
 
3.757052.7%
 
4.4166666677052.7%
 
4.9166666677052.7%
 
5.757052.7%
 
3.8333333337052.7%
 
47052.7%
 
6.9166666674701.8%
 
4.6666666674701.8%
 
104701.8%
 
6.4166666674701.8%
 
2.6666666674701.8%
 
2.754701.8%
 
6.5833333334701.8%
 
3.1666666674701.8%
 
4.54701.8%
 
8.4166666674701.8%
 
4.1666666674701.8%
 
7.3333333334701.8%
 
Other values (46)1081040.7%
 
ValueCountFrequency (%) 
1.3333333332350.9%
 
1.9166666672350.9%
 
22350.9%
 
2.252350.9%
 
2.4166666677052.7%
 
2.52350.9%
 
2.5833333332350.9%
 
2.6666666674701.8%
 
2.754701.8%
 
2.8333333332350.9%
 
ValueCountFrequency (%) 
15.333333332350.9%
 
15.252350.9%
 
14.333333332350.9%
 
14.083333332350.9%
 
13.666666672350.9%
 
13.083333332350.9%
 
12.666666672350.9%
 
11.166666672350.9%
 
10.833333332350.9%
 
104701.8%
 

cdimean
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.232743363
Minimum1
Maximum2.7
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:50.830173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.1
median1.2
Q31.3
95-th percentile1.7
Maximum2.7
Range1.7
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.2611943274
Coefficient of variation (CV)0.2118805384
Kurtosis7.813934317
Mean1.232743363
Median Absolute Deviation (MAD)0.1
Skewness2.227023869
Sum32735.5
Variance0.06822247669
MonotocityNot monotonic
2020-10-22T02:48:50.922383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
1.1658024.8%
 
1634523.9%
 
1.2517019.5%
 
1.423508.8%
 
1.318807.1%
 
1.611754.4%
 
1.511754.4%
 
1.79403.5%
 
2.72350.9%
 
1.82350.9%
 
1.92350.9%
 
22350.9%
 
ValueCountFrequency (%) 
1634523.9%
 
1.1658024.8%
 
1.2517019.5%
 
1.318807.1%
 
1.423508.8%
 
1.511754.4%
 
1.611754.4%
 
1.79403.5%
 
1.82350.9%
 
1.92350.9%
 
ValueCountFrequency (%) 
2.72350.9%
 
22350.9%
 
1.92350.9%
 
1.82350.9%
 
1.79403.5%
 
1.611754.4%
 
1.511754.4%
 
1.423508.8%
 
1.318807.1%
 
1.2517019.5%
 

moodgood
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.407079646
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:51.007638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q36
95-th percentile7
Maximum7
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.061302962
Coefficient of variation (CV)0.1962802531
Kurtosis-0.2753124766
Mean5.407079646
Median Absolute Deviation (MAD)1
Skewness-0.5556909812
Sum143585
Variance1.126363976
MonotocityNot monotonic
2020-10-22T02:48:51.100461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
61222046.0%
 
4587522.1%
 
5470017.7%
 
7305511.5%
 
34701.8%
 
22350.9%
 
ValueCountFrequency (%) 
22350.9%
 
34701.8%
 
4587522.1%
 
5470017.7%
 
61222046.0%
 
7305511.5%
 
ValueCountFrequency (%) 
7305511.5%
 
61222046.0%
 
5470017.7%
 
4587522.1%
 
34701.8%
 
22350.9%
 

moodhappy
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.132743363
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:51.180463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.237380065
Coefficient of variation (CV)0.2410757712
Kurtosis-0.071495652
Mean5.132743363
Median Absolute Deviation (MAD)1
Skewness-0.5893707904
Sum136300
Variance1.531109424
MonotocityNot monotonic
2020-10-22T02:48:51.257369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
61034038.9%
 
4611023.0%
 
5493518.6%
 
725859.7%
 
321158.0%
 
22350.9%
 
12350.9%
 
ValueCountFrequency (%) 
12350.9%
 
22350.9%
 
321158.0%
 
4611023.0%
 
5493518.6%
 
61034038.9%
 
725859.7%
 
ValueCountFrequency (%) 
725859.7%
 
61034038.9%
 
5493518.6%
 
4611023.0%
 
321158.0%
 
22350.9%
 
12350.9%
 

moodrelaxed
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.619469027
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:51.335502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.47139805
Coefficient of variation (CV)0.3185210338
Kurtosis-0.9341278059
Mean4.619469027
Median Absolute Deviation (MAD)1
Skewness-0.3431135151
Sum122670
Variance2.165012221
MonotocityNot monotonic
2020-10-22T02:48:51.421508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6869532.7%
 
3634523.9%
 
5493518.6%
 
4352513.3%
 
714105.3%
 
211754.4%
 
14701.8%
 
ValueCountFrequency (%) 
14701.8%
 
211754.4%
 
3634523.9%
 
4352513.3%
 
5493518.6%
 
6869532.7%
 
714105.3%
 
ValueCountFrequency (%) 
714105.3%
 
6869532.7%
 
5493518.6%
 
4352513.3%
 
3634523.9%
 
211754.4%
 
14701.8%
 

stateanxiety
Real number (ℝ≥0)

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.42477876
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:51.520591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile26
Q129
median31
Q333
95-th percentile42
Maximum50
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.851521774
Coefficient of variation (CV)0.1543852325
Kurtosis2.331904871
Mean31.42477876
Median Absolute Deviation (MAD)2
Skewness1.035026131
Sum834485
Variance23.53726352
MonotocityNot monotonic
2020-10-22T02:48:51.623452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
30352513.3%
 
31352513.3%
 
28305511.5%
 
29282010.6%
 
3221158.0%
 
3418807.1%
 
3314105.3%
 
2614105.3%
 
2711754.4%
 
3811754.4%
 
359403.5%
 
377052.7%
 
204701.8%
 
404701.8%
 
434701.8%
 
212350.9%
 
242350.9%
 
422350.9%
 
502350.9%
 
442350.9%
 
472350.9%
 
ValueCountFrequency (%) 
204701.8%
 
212350.9%
 
242350.9%
 
2614105.3%
 
2711754.4%
 
28305511.5%
 
29282010.6%
 
30352513.3%
 
31352513.3%
 
3221158.0%
 
ValueCountFrequency (%) 
502350.9%
 
472350.9%
 
442350.9%
 
434701.8%
 
422350.9%
 
404701.8%
 
3811754.4%
 
377052.7%
 
359403.5%
 
3418807.1%
 

traitanxiety
Real number (ℝ≥0)

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.99115044
Minimum22
Maximum60
Zeros0
Zeros (%)0.0%
Memory size207.6 KiB
2020-10-22T02:48:51.726995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile24
Q130
median35
Q342
95-th percentile50
Maximum60
Range38
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.238308937
Coefficient of variation (CV)0.2288981829
Kurtosis-0.3881852794
Mean35.99115044
Median Absolute Deviation (MAD)6
Skewness0.403145206
Sum955745
Variance67.86973414
MonotocityNot monotonic
2020-10-22T02:48:51.833329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
3223508.8%
 
3818807.1%
 
3314105.3%
 
3011754.4%
 
3411754.4%
 
3611754.4%
 
4311754.4%
 
3111754.4%
 
2611754.4%
 
419403.5%
 
499403.5%
 
359403.5%
 
379403.5%
 
249403.5%
 
449403.5%
 
429403.5%
 
227052.7%
 
287052.7%
 
257052.7%
 
477052.7%
 
274701.8%
 
234701.8%
 
404701.8%
 
514701.8%
 
294701.8%
 
Other values (7)21158.0%
 
ValueCountFrequency (%) 
227052.7%
 
234701.8%
 
249403.5%
 
257052.7%
 
2611754.4%
 
274701.8%
 
287052.7%
 
294701.8%
 
3011754.4%
 
3111754.4%
 
ValueCountFrequency (%) 
602350.9%
 
552350.9%
 
542350.9%
 
514701.8%
 
502350.9%
 
499403.5%
 
484701.8%
 
477052.7%
 
462350.9%
 
454701.8%
 

Interactions

2020-10-22T02:47:54.274081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:54.402790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:54.521690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:54.633713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:54.732822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:54.839269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:54.951162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.064034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.182329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.297395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.408407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.509436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.636961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.744316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.849738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:55.964409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:56.096537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:56.218584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:56.325016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:56.421428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:56.518418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:56.631323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:56.753522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:56.889198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:57.022341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:57.146510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:57.273165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:57.394527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:57.491438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:57.609674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:57.739678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:57.868736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:58.000301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:58.275517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:58.412666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:58.516972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:58.645361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:58.761471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:58.861969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:58.983691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:59.118733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:59.270777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:59.406235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:59.522013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:59.637491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:59.750853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:59.856001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:47:59.949500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.048918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.146967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.254106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.359545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.465803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.590474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.694664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.801132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.887824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:00.981503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.078488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.184738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.294391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.391038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.488986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.587801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.668519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.772833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.866324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:01.958962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.058451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.153890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.252151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.347896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.448067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.545353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.637695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.878486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:02.999208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.101874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.216356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.332157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.436165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.547371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.661436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.774704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.884478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:03.986457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.085163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.180258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.276250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.365310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.454122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.563904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.672188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.774284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.872102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:04.977784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.095144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.219406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.328972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.442547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.559633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.667664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.775956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.871756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:05.986793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.107799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.206070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.310782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.406785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.493694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.585262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.688089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.790480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.885654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:06.975680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.071658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.174652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.275423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.378562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.464908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.557481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.656247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.757587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.866941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:07.988487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:08.108667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:08.228654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:08.519182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:08.643063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:08.751361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:08.854279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:08.944386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.042030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.133812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.243215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.370847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.504685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.621984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.720521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.820092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:09.926508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:10.051089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-10-22T02:48:33.618514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:33.740869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:33.859328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:33.983192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.086638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.185470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.290229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.382867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.473831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.571836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.677781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.789148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:34.904938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:35.019798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:35.132655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:35.245595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:35.351865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:35.455460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:35.564773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:36.029988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:36.164786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:36.303129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:36.456212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:36.606998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:36.736707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:36.846489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:36.978936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:37.118819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:37.282282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:37.403696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:37.508551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:37.634484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:37.770525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:37.875887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:37.966984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:38.059755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:38.180632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:38.331438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:38.454811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:38.553321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:38.659123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:38.799428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:38.961537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:39.148804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:39.274680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:39.405998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:39.543475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:39.669271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:39.826478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:39.954775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:40.056574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:40.162756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:40.291321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:40.418353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:40.560953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:40.715009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:40.828553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:40.929027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:41.052008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:41.197033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:41.342173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:41.443456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:41.544004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:41.645840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:41.785967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:41.925770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.098324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.232428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.325454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.456770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.584283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.681217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.778050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.873396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:42.976726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:43.079499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:43.199138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:43.349512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:43.475940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:43.572379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:43.674404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:43.820955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:43.985168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:44.105186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:44.218840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:44.354997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:44.477556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:44.623038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:44.744395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:44.852152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:44.946026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:45.061278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:45.190949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-10-22T02:48:51.966548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-22T02:48:52.207392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-22T02:48:52.425450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-22T02:48:52.657473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-22T02:48:52.837202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-22T02:48:45.472148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:45.823472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-22T02:48:46.127225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

idsexmosyrsagelabel_encAvg_deggenderpeermindsetpersmindsetneedforapprovalneedforbelongingrejectioncoping_madcoping_sadcoping_worriedrsqanxietyrsqangercdimeanmoodgoodmoodhappymoodrelaxedstateanxietytraitanxiety
0102_9afemale110.39.14900.0female1.671.03.753.74.04.05.03.07.174.671.07672731
1102_9afemale110.39.14910.0female1.671.03.753.74.04.05.03.07.174.671.07672731
2102_9afemale110.39.14920.0female1.671.03.753.74.04.05.03.07.174.671.07672731
3102_9afemale110.39.14930.0female1.671.03.753.74.04.05.03.07.174.671.07672731
4102_9afemale110.39.14940.0female1.671.03.753.74.04.05.03.07.174.671.07672731
5102_9afemale110.39.14950.0female1.671.03.753.74.04.05.03.07.174.671.07672731
6102_9afemale110.39.14960.0female1.671.03.753.74.04.05.03.07.174.671.07672731
7102_9afemale110.39.14970.0female1.671.03.753.74.04.05.03.07.174.671.07672731
8102_9afemale110.39.14980.0female1.671.03.753.74.04.05.03.07.174.671.07672731
9102_9afemale110.39.14990.0female1.671.03.753.74.04.05.03.07.174.671.07672731

Last rows

idsexmosyrsagelabel_encAvg_deggenderpeermindsetpersmindsetneedforapprovalneedforbelongingrejectioncoping_madcoping_sadcoping_worriedrsqanxietyrsqangercdimeanmoodgoodmoodhappymoodrelaxedstateanxietytraitanxiety
26545531_17amale206.7117.121722596.51male3.422.03.52.82.52.02.332.337.53.081.16633028
26546531_17amale206.7117.121722698.15male3.422.03.52.82.52.02.332.337.53.081.16633028
26547531_17amale206.7117.121722797.72male3.422.03.52.82.52.02.332.337.53.081.16633028
26548531_17amale206.7117.121722897.98male3.422.03.52.82.52.02.332.337.53.081.16633028
26549531_17amale206.7117.121722996.59male3.422.03.52.82.52.02.332.337.53.081.16633028
26550531_17amale206.7117.121723097.37male3.422.03.52.82.52.02.332.337.53.081.16633028
26551531_17amale206.7117.121723198.00male3.422.03.52.82.52.02.332.337.53.081.16633028
26552531_17amale206.7117.121723295.53male3.422.03.52.82.52.02.332.337.53.081.16633028
26553531_17amale206.7117.121723396.83male3.422.03.52.82.52.02.332.337.53.081.16633028
26554531_17amale206.7117.121723498.70male3.422.03.52.82.52.02.332.337.53.081.16633028